Artificial intelligence modeling of small molecule pharmaceutical solubility in supercritical fluid and comparative...

Enhancing the dissolution behavior of poorly water-soluble pharmaceuticals remains an important challenge in drug formulation and manufacturing, since limited aqueous solubility can substantially restrict therapeutic performance. Supercritical-fluid technologies provide a promising route for addressing this limitation, particularly through the production of nanoscale drug formulations with improved dissolution characteristics. In the present work, the ternary solubility of Nystatin in the SC-CO₂-ethanol system was modeled as a function of temperature and pressure. Four regression approaches-K-nearest neighbors, linear regression, multilayer perceptron (MLP), and Theil-Sen regression-were examined as individual learners and subsequently incorporated into AdaBoost-based ensemble models. Model-specific hyperparameters were tuned using Glowworm Swarm Optimization whose population-based search strategy is suitable for exploring heterogeneous optimization spaces containing different parameter types. The developed ensemble configurations demonstrated strong agreement with the experimental solubility data, as reflected by high coefficients of determination and low root mean square errors. Among all evaluated models, the AdaBoost-MLP configuration provided the most favorable predictive performance, achieving an R ² of 0.99412 together with the lowest root mean square error.